Method of creating map data comprising transit times for intersections

ABSTRACT

A computerised method of creating map data from position data derived from the positions ( 606 ) of at least one vehicle over a period of time, the map data comprising a plurality of navigable segments representing segments ( 602,604 ) of a navigable route ( 607 ) in the area covered by the map and the map data also comprising intersections between navigable segments ( 602,604 ) representing intersections in the navigable route, the method comprising using a processing circuitry to perform the following steps:
         i. processing the position data;   ii. calculating from the processing of the position data a transit time or set of transit times for at least some of the intersections in the map data; and   iii generating further map data, which for at least some of the intersections therein, contains the calculated transit time or set of transit times associated with the intersection for which the calculation was made.

FIELD OF THE INVENTION

The invention relates to a method of processing positioning data and inparticular to processing positioning data in order to generate map dataarranged to be used in navigation devices and in particular, but notespecially in a Portable Navigation Device (PND). The invention alsoprovides related apparatus for providing the method.

BACKGROUND TO THE INVENTION

Map data for electronic navigation devices, such as GPS based personalnavigation devices like the GO™ from TomTom International BV, comes fromspecialist map vendors such as Tele Atlas NV. Such devices are alsoreferred to as Portable Navigation Devices (PND's). This map data isspecially designed to be used by route guidance algorithms, typicallyusing location data from the GPS system. For example, roads can bedescribed as lines—i.e. vectors (e.g. start point, end point, directionfor a road, with an entire road being made up of many hundreds of suchsegments, each uniquely defined by start point/end point directionparameters). A map is then a set of such road segments, data associatedwith each segment (speed limit; travel direction, etc.) plus points ofinterest (POI's), plus road names, plus other geographic features likepark boundaries, river boundaries, etc., all of which are defined interms of vectors. All map features (e.g. road segments, POI's etc.) aretypically defined in a co-ordinate system that corresponds with orrelates to the GPS co-ordinate system, enabling a device's position asdetermined through a GPS system to be located onto the relevant roadshown in a map and for an optimal route to be planned to a destination.

To construct this map database, Tele Atlas utilises basic roadinformation from various sources, such as the Ordnance Survey for roadsin England. It also includes, but is not limited to, the deployment of alarge, dedicated team of vehicles driving on roads, plus personnelchecking other maps and aerial photographs, to update and check itsdata. This data constitutes the core of the Tele Atlas map database.This map database is being continuously enhanced with geo-referenceddata. It is then checked and published multiple times a year to devicemanufacturers like TomTom.

Each such road segment has associated therewith speed data for that roadsegment which gives an indication of the speed at which a vehicle cantravel along that segment and is an average speed generated by the partythat produced the map data, which may be, for example, Tele Atlas. Thespeed data is used by route planning algorithms on PND's, or otherdevices, on which the map is processed. The accuracy of such routeplanning thus depends on the accuracy of the speed data. For example, auser is often presented with an option on his/her PND to have itgenerate the fastest route between the current location of the deviceand a destination. The route calculated by the PND may well not be thefastest route if the speed data is inaccurate.

It is known that parameters such as density of traffic can significantlyeffect the speed profile of a segment of road and such speed profilevariations mean that the quickest route between two points may notremain the same. Inaccuracies in the speed parameter of a road segmentcan also lead to inaccurate Estimated Times of Arrival (ETA) as well asselection of a sub-optimal quickest route.

The map data also contains a time allowance for intersections betweenroad segments. These time allowances tend to be fixed values for a givencategory of road segment on which they occur. Routing algorithms arearranged to use this time allowance when they are processing the mapdata to determine a route.

Tele Atlas has developed a system in which GPS data is uploaded fromPND's and used to provide speed parameters for segments of the map datawhich aims to provide speed parameters which show the averaged truespeed on a road segment at predetermined times of a day.

SUMMARY OF THE INVENTION

According to a first aspect of the invention there is provided acomputerised method of creating map data from position data derived fromthe positions of at least one vehicle over a period of time, the mapdata comprising a plurality of navigable segments representing segmentsof a navigable route in the area covered by the map and the map dataalso comprising intersections between navigable segments representingintersections in the navigable route, the method comprising using aprocessing circuitry to perform the following steps:

-   -   i. processing the position data;    -   ii. calculating from the processing of the position data a        transit time or set of transit times for at least some of the        intersections in the map data; and    -   iii. generating further map data, which for at least some of the        intersections therein, contains the calculated transit time or        set of transit times associated with the intersection for which        the calculation was made.

Determining the transit time for an intersection from position dataallows more specific transit times to be allocated for an intersection.Transit time through an intersection is a more complex property of theroad network when compared to a transit time of a road segment; it isnot an attribute of a given road segment, but a property of a drivingmaneuver across multiple road segments. Such “costs” can be considerableunder certain circumstances, a prime example of which is the waitingtime for a vehicle that wants to enter, or cross, a heavy-traffic road(e.g., during rush hours) that has right-of-way. Thus, increasing theaccuracy of the transit time allows more accurate routes to be plannedby devices using the map data.

The skilled person will appreciate that for any map that represents anavigable route by segments there will be a need to have boundariesbetween those segments; ie any route planned using that map will need tomover across a plurality of segments unless the route is short. It isconvenient if those boundaries are provided at intersections in thenavigable route but this need not be the case. However, embodiments ofthe invention may be utilised to determine a transit time or set oftransit times for a transition between segments however those segmentsare arranged. That is the transit time between segments may not bederivable from the average speed, or speed profile, for segments overwhich the transition is occurring.

The method may analyse the position data in order to identify trips ofvehicles within the position data.

The method may be arranged to determine routes through the intersectionand to analyse the position data, and generally the trips identifiedwithin the position data, to determine trips that pass through thedetermined routes through the intersection. As discussed above differentroutes through any one intersection can have significantly differenttransit times; for example a left hand turn across on-coming traffic islikely to be significantly slower than a left hand turn that is notacross traffic.

The or each trips passing through a determined route may subsequently beanalyzed to determine the time taken for the or each trip to passthrough the intersection; ie to determine the transit time, or at leastto provide an estimation of the transit time.

The times for the trips that have been determined to have taken one ofthe predetermined routes may be averaged, thereby giving a more accurateoverall time for that predetermined trip.

In some embodiments, the averages may be taken within predetermined timeperiods. In one embodiment, the trips are averaged according to the timeat which the trip was made. Conveniently, the trips are averaged overperiods of a predetermined length. It will be appreciated that trafficflow can vary significantly over the course of a day and as such usingpredetermined periods may increase the accuracy.

Some embodiments of the method may be arranged to analyse the averagesto determine the quality of the average. If it is determined that thequality does not meet predetermined criteria then the averages may bemodified.

In some embodiments, the length of the predetermined period of time maybe increased thereby increasing the number of trips that fall within anyone time period.

The method may be further arranged to analyse trips through anintersection into at least one of a pre intersection zones; interintersections zones; and post intersection zones. The method may furthercomprise utilizing one or more of the pre, inter and post intersectionzones as the transit time associated with an intersection.

Conveniently, the method applies a bounding box to road segments aroundan intersection. Such a method is convenient to determine this positiondata should be considered to be part of a path through an intersection.

The bounding box may be determined in terms of a predetermined distancefrom a point in the intersection. For example, the bounding box may bedetermined to be roughly 150 m from the intersection. The skilled personwill appreciate that other distances may be suitable, such as roughlyany of the following: 50 m, 75 m, 100 m, 125 m, 175 m, 200 m.

Alternatively, or additionally, the bounding box may be determined interms of transit time from a point determined to be the intersection.

Intersections may be categorized according to a classification and thetimes taken for a trip to pass through intersections within the same, orat least similar categories, may be averaged. Such a method may beapplied to averages previously determined in an aim to improve thataverage. Alternatively, or additionally, the classification may be usedto determine the average.

The method may be arranged to use the trips derived from the positiondata to determine a set of equations in which the transit time throughthe intersections are unknown variables.

Further, the method may be arranged to process the set of equations todetermine a transit time for at least some intersections. In someembodiments, the method determines the transit time by minimizing theerror in the set of equations by determining a transit time for eachcategory of intersection.

According to a second aspect of the invention there is provided amachine arranged to create map data comprising a plurality of navigablesegments, representing segments of a navigable route in the area coveredby the map, and intersections, representing intersection in thenavigable route covered by the map, the machine comprising processingcircuitry arranged to process position data derived from the positionsof at least one vehicle over a period of time, the processing circuitrybeing programmed to:

-   -   i. process the position data;    -   ii. calculate from the processing of the position data a transit        time or set of transit times for at least some of the        intersections in the map data; and    -   iii. generate further map data, which for at least some of the        intersections therein, contains the calculated transit time or        set of transit times associated with the intersection for which        the calculation was made.

According to a third aspect of the invention there is provided map datacomprising a plurality of navigable segments, representing segments of anavigable route in the area covered by the map, and intersections,representing intersections between the navigable segments covered by themap wherein at least some of the intersections have a transit timeassociated therewith that has been derived by averaging position datausing the method of the first aspect of the invention.

According to a fourth aspect of the invention there is provided amachine readable medium containing instructions which when read by amachine cause that machine to perform the method of the first aspect ofthe invention.

According to a fifth aspect of the invention there is provided a machinereadable medium containing instructions which when read by a machinecause that machine of function as the machine of the second aspect ofthe invention.

According to a sixth aspect of the invention there is provided a machinereadable medium containing instructions which contain the map data ofthe third aspect of the invention.

According to a seventh aspect of the invention there is providedcomputerised method of creating map data from position data derived fromthe positions of at least one vehicle over a period of time, the mapdata comprising a plurality of navigable segments representing segmentsof a navigable route in the area covered by the map and the map dataalso comprising intersections between navigable segments representingintersections in the navigable route, the method comprising using aprocessing circuitry to perform the following steps:

-   -   i. processing the position data;    -   ii. calculating from the processing of the position data a        transit time or set of transit times for at least some of the        intersections in the map data;    -   iii. clustering the calculated transit times within        predetermined time periods in order to generate a transit time        profile from the transit times; and    -   iv. generating further map data containing the transit time        profiles for intersections that have the transit time profile        determined with intersections therein.

According to an eight aspect of the invention there is provided acomputerised method of creating map data from position data derived fromthe positions of at least one vehicle over a period of time, the mapdata comprising a plurality of navigable segments representing segmentsof a navigable route in the area covered by the map and the map dataalso comprising intersections between navigable segments representingintersections in the navigable route, the method comprising using aprocessing circuitry to perform the following steps:

-   -   i. processing the position data;    -   ii. generating a set of equations from the processing of the        position data in which transit times for intersections are an        unknown variable;    -   iii. calculating from the set of equations a transit time for at        least some of the intersections in the map data; and    -   iv. generating further map data containing the transit time for        intersections that have the transit time calculated with        intersections therein.

In any of the above aspects of the invention the machine readable mediummay comprise any of the following: a floppy disk, a CD ROM, a DVDROM/RAM (including a −R/−RW and +R/+RW), a hard drive, a solid statememory (including a USB memory key, an SD card, a Memorystick™, acompact flash card, or the like), a tape, any other form of magnetooptical storage, a transmitted signal (including an Internet download,an FTP transfer, etc), a wire, or any other suitable medium.

Further, the skilled person will appreciate that features discussed inrelation to any one aspects of the invention are suitable, mutatismutandis, for other aspects of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

Various aspects of the teachings of the present invention, andarrangements embodying those teachings, will hereafter be described byway of illustrative example with reference to the accompanying drawings,in which:

FIG. 1 is a schematic illustration of a Global Positioning System (GPS);

FIG. 2 is a schematic illustration of electronic components arranged toprovide a navigation device;

FIG. 3 is a schematic illustration of the manner in which a navigationdevice may receive information over a wireless communication channel;

FIGS. 4A and 4B are illustrative perspective views of a navigationdevice;

FIG. 5 is a schematic representation of the software employed by thenavigation device;

FIG. 6 shows, schematically, an intersection between two road segments;

FIG. 7 shows a first example intersection;

FIG. 8 shows a second example intersection, more complex than the first;

FIG. 9 shows a plan of a route between various nodes; and

FIGS. 10 and 11 shows flow charts outlining embodiments of theinvention.

DETAILED DESCRIPTION OF EMBODIMENTS OF INVENTION

Embodiments of the present invention will now be described withparticular reference to a PND (Portable Navigation Device). It should beremembered, however, that the teachings of the present invention are notlimited to PND's but are instead universally applicable to any type ofprocessing device that is configured to execute navigation software soas to provide route planning and navigation functionality. It followstherefore that in the context of the present application, a navigationdevice is intended to include (without limitation) any type of routeplanning and navigation device, irrespective of whether that device isembodied as a PND, a navigation device built into a vehicle, a mapserver (such as providing routing and navigation functionality over theinternet), or indeed a computing resource (such as a desktop or portablepersonal computer (PC), mobile telephone or portable digital assistant(PDA)) executing route planning and navigation software.

It will also be apparent from the following that the teachings of thepresent invention even have utility in circumstances where a user is notseeking instructions on how to navigate from one point to another, butmerely wishes to be provided with a view of a given location. In suchcircumstances the “destination” location selected by the user need nothave a corresponding start location from which the user wishes to startnavigating, and as a consequence references herein to the “destination”location or indeed to a “destination” view should not be interpreted tomean that the generation of a route is essential, that travelling to the“destination” must occur, or indeed that the presence of a destinationrequires the designation of a corresponding start location.

With the above provisos in mind, FIG. 1 illustrates an example view ofGlobal Positioning System (GPS), usable by navigation devices. Suchsystems are known and are used for a variety of purposes. In general,GPS is a satellite-radio based navigation system capable of determiningcontinuous position, velocity, time, and in some instances directioninformation for an unlimited number of users. Formerly known as NAVSTAR,the GPS incorporates a plurality of satellites which orbit the earth inextremely precise orbits. Based on these precise orbits, GPS satellitescan relay their location to any number of receiving units. However, itwill be understood that Global Positioning systems could be used, suchas GLOSNASS, the European Galileo positioning system, COMPASSpositioning system or IRNSS (Indian Regional Navigational SatelliteSystem).

The GPS system is implemented when a device, specially equipped toreceive GPS data, begins scanning radio frequencies for GPS satellitesignals. Upon receiving a radio signal from a GPS satellite, the devicedetermines the precise location of that satellite via one of a pluralityof different conventional methods. The device will continue scanning, inmost instances, for signals until it has acquired at least threedifferent satellite signals (noting that position is not normally, butcan be determined, with only two signals using other triangulationtechniques). Implementing geometric triangulation, the receiver utilizesthe three known positions to determine its own two-dimensional positionrelative to the satellites. This can be done in a known manner.Additionally, acquiring a fourth satellite signal will allow thereceiving device to calculate its three dimensional position by the samegeometrical calculation in a known manner. The position and velocitydata can be updated in real time on a continuous basis by an unlimitednumber of users.

As shown in FIG. 1, the GPS system is denoted generally by referencenumeral 100. A plurality of satellites 120 are in orbit about the earth124. The orbit of each satellite 120 is not necessarily synchronous withthe orbits of other satellites 120 and, in fact, is likely asynchronous.A GPS receiver 140 is shown receiving spread spectrum GPS satellitesignals 160 from the various satellites 120.

The spread spectrum signals 160, continuously transmitted from eachsatellite 120, utilize an accurate frequency standard accomplished withan accurate atomic clock. Each satellite 120, as part of its data signaltransmission 160, transmits a data stream indicative of that particularsatellite 120. It is appreciated by those skilled in the relevant artthat the GPS receiver device 140 generally acquires spread spectrum GPSsatellite signals 160 from at least three satellites 120 for the GPSreceiver device 140 to calculate its two-dimensional position bytriangulation. Acquisition of an additional signal, resulting in signals160 from a total of four satellites 120, permits the GPS receiver device140 to calculate its three-dimensional position in a known manner.

FIG. 2 is an illustrative representation of electronic components of anavigation device 200 according to a preferred embodiment of the presentinvention, in block component format. It should be noted that the blockdiagram of the navigation device 200 is not inclusive of all componentsof the navigation device, but is only representative of many examplecomponents.

The navigation device 200 is located within a housing (not shown). Thehousing includes a processor 210 connected to an input device 220 and adisplay screen 240. The input device 220 can include a keyboard device,voice input device, touch panel and/or any other known input deviceutilised to input information; and the display screen 240 can includeany type of display screen such as an LCD display, for example. In aparticularly preferred arrangement the input device 220 and displayscreen 240 are integrated into an integrated input and display device,including a touchpad or touch screen input so that a user need onlytouch a portion of the display screen 240 to select one of a pluralityof display choices or to activate one of a plurality of virtual buttons.

The navigation device may include an output device 260, for example anaudible output device (e.g. a loudspeaker). As output device 260 canproduce audible information for a user of the navigation device 200, itis should equally be understood that input device 240 can include amicrophone and software for receiving input voice commands as well.

In the navigation device 200, processor 210 is operatively connected toand set to receive input information from input device 220 via aconnection 225, and operatively connected to at least one of displayscreen 240 and output device 260, via output connections 245, to outputinformation thereto. Further, the processor 210 is operably coupled to amemory resource 230 via connection 235 and is further adapted toreceive/send information from/to input/output (I/O) ports 270 viaconnection 275, wherein the I/O port 270 is connectible to an I/O device280 external to the navigation device 200. The memory resource 230comprises, for example, a volatile memory, such as a Random AccessMemory (RAM) and a non-volatile memory, for example a digital memory,such as a flash memory.

The processor 210 may also communicate with a port 234, via a connection236, into which a removable memory card (commonly referred to as a card)may be added to the device 200. In the embodiment being described theport is arranged to allow an SD (Secure Digital) card to be added. Inother embodiments, the port may allow other formats of memory to beconnected (such as Compact Flash (CF) cards, Memory Sticks™, xD memorycards, USB (Universal Serial Bus) Flash drives, MMC (MultiMedia) cards,SmartMedia cards, Microdrives, or the like).

The external I/O device 280 may include, but is not limited to anexternal listening device such as an earpiece for example. Theconnection to I/O device 280 can further be a wired or wirelessconnection to any other external device such as a car stereo unit forhands-free operation and/or for voice activated operation for example,for connection to an ear piece or head phones, and/or for connection toa mobile phone for example, wherein the mobile phone connection may beused to establish a data connection between the navigation device 200and the internet or any other network for example, and/or to establish aconnection to a server via the internet or some other network forexample.

FIG. 2 further illustrates an operative connection between the processor210 and an antenna/receiver 250 via connection 255, wherein theantenna/receiver 250 can be a GPS antenna/receiver for example. It willbe understood that the antenna and receiver designated by referencenumeral 250 are combined schematically for illustration, but that theantenna and receiver may be separately located components, and that theantenna may be a GPS patch antenna or helical antenna for example.

Further, it will be understood by one of ordinary skill in the art thatthe electronic components shown in FIG. 2 are powered by power sources(not shown) in a conventional manner. As will be understood by one ofordinary skill in the art, different configurations of the componentsshown in FIG. 2 are considered to be within the scope of the presentapplication. For example, the components shown in FIG. 2 may be incommunication with one another via wired and/or wireless connections andthe like. Thus, the scope of the navigation device 200 of the presentapplication includes a portable or handheld navigation device 200.

In addition, the portable or handheld navigation device 200 of FIG. 2can be connected or “docked” in a known manner to a vehicle such as abicycle, a motorbike, a car or a boat for example. Such a navigationdevice 200 is then removable from the docked location for portable orhandheld navigation use.

Referring now to FIG. 3, the navigation device 200 may establish a“mobile” or telecommunications network connection with a server 302 viaa mobile device (not shown) (such as a mobile phone, PDA, and/or anydevice with mobile phone technology) establishing a digital connection(such as a digital connection via known Bluetooth technology forexample). Thereafter, through its network service provider, the mobiledevice can establish a network connection (through the internet forexample) with a server 302. As such, a “mobile” network connection isestablished between the navigation device 200 (which can be, and oftentimes is mobile as it travels alone and/or in a vehicle) and the server302 to provide a “real-time” or at least very “up to date” gateway forinformation.

The establishing of the network connection between the mobile device(via a service provider) and another device such as the server 302,using an internet (such as the World Wide Web) for example, can be donein a known manner. This can include use of TCP/IP layered protocol forexample. The mobile device can utilize any number of communicationstandards such as CDMA, GSM, WAN, etc.

As such, an internet connection may be utilised which is achieved viadata connection, via a mobile phone or mobile phone technology withinthe navigation device 200 for example. For this connection, an internetconnection between the server 302 and the navigation device 200 isestablished. This can be done, for example, through a mobile phone orother mobile device and a GPRS (General Packet Radio Service)-connection(GPRS connection is a high-speed data connection for mobile devicesprovided by telecom operators; GPRS is a method to connect to theinternet).

The navigation device 200 can further complete a data connection withthe mobile device, and eventually with the internet and server 302, viaexisting Bluetooth technology for example, in a known manner, whereinthe data protocol can utilize any number of standards, such as the GSRM,the Data Protocol Standard for the GSM standard, for example.

The navigation device 200 may include its own mobile phone technologywithin the navigation device 200 itself (including an antenna forexample, or optionally using the internal antenna of the navigationdevice 200). The mobile phone technology within the navigation device200 can include internal components as specified above, and/or caninclude an insertable card (e.g. Subscriber Identity Module or SIMcard), complete with necessary mobile phone technology and/or an antennafor example. As such, mobile phone technology within the navigationdevice 200 can similarly establish a network connection between thenavigation device 200 and the server 302, via the internet for example,in a manner similar to that of any mobile device.

For GRPS phone settings, a Bluetooth enabled navigation device may beused to correctly work with the ever changing spectrum of mobile phonemodels, manufacturers, etc., model/manufacturer specific settings may bestored on the navigation device 200 for example. The data stored forthis information can be updated.

In FIG. 3 the navigation device 200 is depicted as being incommunication with the server 302 via a generic communications channel318 that can be implemented by any of a number of differentarrangements. The server 302 and a navigation device 200 can communicatewhen a connection via communications channel 318 is established betweenthe server 302 and the navigation device 200 (noting that such aconnection can be a data connection via mobile device, a directconnection via personal computer via the internet, etc.).

The server 302 includes, in addition to other components which may notbe illustrated, a processor 304 operatively connected to a memory 306and further operatively connected, via a wired or wireless connection314, to a mass data storage device 312. The processor 304 is furtheroperatively connected to transmitter 308 and receiver 310, to transmitand send information to and from navigation device 200 viacommunications channel 318. The signals sent and received may includedata, communication, and/or other propagated signals. The transmitter308 and receiver 310 may be selected or designed according to thecommunications requirement and communication technology used in thecommunication design for the navigation system 200. Further, it shouldbe noted that the functions of transmitter 308 and receiver 310 may becombined into a signal transceiver.

Server 302 is further connected to (or includes) a mass storage device312, noting that the mass storage device 312 may be coupled to theserver 302 via communication link 314. The mass storage device 312contains a store of navigation data and map information, and can againbe a separate device from the server 302 or can be incorporated into theserver 302.

The navigation device 200 is adapted to communicate with the server 302through communications channel 318, and includes processor, memory, etc.as previously described with regard to FIG. 2, as well as transmitter320 and receiver 322 to send and receive signals and/or data through thecommunications channel 318, noting that these devices can further beused to communicate with devices other than server 302. Further, thetransmitter 320 and receiver 322 are selected or designed according tocommunication requirements and communication technology used in thecommunication design for the navigation device 200 and the functions ofthe transmitter 320 and receiver 322 may be combined into a singletransceiver.

Software stored in server memory 306 provides instructions for theprocessor 304 and allows the server 302 to provide services to thenavigation device 200. One service provided by the server 302 involvesprocessing requests from the navigation device 200 and transmittingnavigation data from the mass data storage 312 to the navigation device200. Another service provided by the server 302 includes processing thenavigation data using various algorithms for a desired application andsending the results of these calculations to the navigation device 200.

The communication channel 318 generically represents the propagatingmedium or path that connects the navigation device 200 and the server302. Both the server 302 and navigation device 200 include a transmitterfor transmitting data through the communication channel and a receiverfor receiving data that has been transmitted through the communicationchannel.

The communication channel 318 is not limited to a particularcommunication technology. Additionally, the communication channel 318 isnot limited to a single communication technology; that is, the channel318 may include several communication links that use a variety oftechnology. For example, the communication channel 318 can be adapted toprovide a path for electrical, optical, and/or electromagneticcommunications, etc. As such, the communication channel 318 includes,but is not limited to, one or a combination of the following: electriccircuits, electrical conductors such as wires and coaxial cables, fibreoptic cables, converters, radio-frequency (RF) waves, the atmosphere,empty space, etc. Furthermore, the communication channel 318 can includeintermediate devices such as routers, repeaters, buffers, transmitters,and receivers, for example.

In one illustrative arrangement, the communication channel 318 includestelephone and computer networks. Furthermore, the communication channel318 may be capable of accommodating wireless communication such as radiofrequency, microwave frequency, infrared communication, etc.Additionally, the communication channel 318 can accommodate satellitecommunication.

The communication signals transmitted through the communication channel318 include, but are not limited to, signals as may be required ordesired for given communication technology. For example, the signals maybe adapted to be used in cellular communication technology such as TimeDivision Multiple Access (TDMA), Frequency Division Multiple Access(FDMA), Code Division Multiple Access (CDMA), Global System for MobileCommunications (GSM), etc. Both digital and analogue signals can betransmitted through the communication channel 318. These signals may bemodulated, encrypted and/or compressed signals as may be desirable forthe communication technology.

The server 302 includes a remote server accessible by the navigationdevice 200 via a wireless channel. The server 302 may include a networkserver located on a local area network (LAN), wide area network (WAN),virtual private network (VPN), etc.

The server 302 may include a personal computer such as a desktop orlaptop computer, and the communication channel 318 may be a cableconnected between the personal computer and the navigation device 200.Alternatively, a personal computer may be connected between thenavigation device 200 and the server 302 to establish an internetconnection between the server 302 and the navigation device 200.Alternatively, a mobile telephone or other handheld device may establisha wireless connection to the internet, for connecting the navigationdevice 200 to the server 302 via the internet.

The navigation device 200 may be provided with information from theserver 302 via information downloads which may be periodically updatedautomatically or upon a user connecting navigation device 200 to theserver 302 and/or may be more dynamic upon a more constant or frequentconnection being made between the server 302 and navigation device 200via a wireless mobile connection device and TCP/IP connection forexample. For many dynamic calculations, the processor 304 in the server302 may be used to handle the bulk of the processing needs, however,processor 210 of navigation device 200 can also handle much processingand calculation, oftentimes independent of a connection to a server 302.

Further, the processor 210 is arranged, from time to time, to upload therecord of the whereabouts of the device 200 (ie the GPS data and thetime stamp) to the server 302. In some embodiments in which thenavigation device 200 has a permanent, or at least generally present,communication channel 318 connecting it to the server 302 the uploadingof the data occurs on a periodic basis which may for example be onceevery 24 hours. The skilled person will appreciate that other periodsare possible and may be substantially any of the following periods: 15minutes, 30 minutes, hourly, every 2 hours, every 5 hours, every 12hours, every 2 days, weekly, or any time in between these. Indeed, insuch embodiments the processor 210 may be arranged to upload the recordof the whereabouts on a substantially real time basis, although this mayinevitably mean that data is in fact transmitted from time to time witha relatively short period between the transmissions and as such may bemore correctly thought of as being pseudo real time. In such pseudo realtime embodiments, the navigation device may be arranged to buffer theGPS fixes within the memory 230 and/or on a card inserted in the port234 and to transmit these when a predetermined number have been stored.This predetermined number may be on the order of 20, 36, 100, 200 or anynumber in between. The skilled person will appreciate that thepredetermined number is in part governed by the size of the memory230/card within the port 234.

In other embodiments, which do not have a generally presentcommunication channel 318 the processor 210 may be arranged to uploadthe record to the server 302 when a communication channel 318 iscreated. This may for example, be when the navigation device 200 isconnected to a user's computer. Again, in such embodiments, thenavigation device may be arranged to buffer the GPS fixes within thememory 230 or on a card inserted in the port 234. Should the memory 230or card inserted in the port 234 become full of GPS fixes the navigationdevice may be arranged to deleted the oldest GPS fixes and as such itmay be thought of as a First in First Out (FIFO) buffer.

In the embodiment being described, the record of the whereaboutscomprises one or more traces with each trace representing the movementof that navigation device 200 within a 24 hour period. Each 24 hourperiod is arranged to coincide with a calendar day but in otherembodiments, this need not be the case.

Generally, a user of a navigation device 200 gives his/her consent forthe record of the devices whereabouts to be uploaded to the server 302.If no consent is given then no record is uploaded to the server 302. Thenavigation device itself, and/or a computer to which the navigationdevice is connected may be arranged to ask the user for his/her consentto such use of the record of whereabouts.

The server 302 is arranged to receive the record of the whereabouts ofthe device and to store this within the mass data storage 312 forprocessing. Thus, as time passes the mass data storage 312 accumulates aplurality of records of the whereabouts of navigation devices 200 whichhave uploaded data.

The mass data storage 312 also contains map data. Such map data providesinformation about the location of road segments, points of interest andother such information that is generally found on map.

As a first process, the server 302 is arranged to perform a map matchingfunction between the map data and the GPS fixes contained within therecords of the whereabouts that have been received. Such map matchingmay be performed in a so-called real time manner; ie as the records ofwhereabouts are received or may be performed a time later after therecords of the whereabouts have been recalled from the mass data storage312.

In order to increase the accuracy of the map matching, pre-processing ofthe records of the whereabouts is performed as follows. Each GPS trace(ie a 24 hour period of GPS data) is divided into one or more trips witheach trip representing a single journey of the navigation device 200which are subsequently stored for later processing.

Within each trip GPS fixes whose accuracy report received from thenavigation device is not sufficiently high are rejected. Thus, in someembodiments, a fix may be rejected if the accuracy report indicates thatthe signals from less than three satellites 102 were being received bythe navigation device 200 in relation to that GPS fix. Further, eachtrip is clipped when the reported time between fixes goes above athreshold value. Each trip that passes this pre-processing stage ispassed to be map matched.

As indicated above in FIG. 2, a navigation device 200 includes aprocessor 210, an input device 220, and a display screen 240. The inputdevice 220 and display screen 240 are integrated into an integratedinput and display device to enable both input of information (via directinput, menu selection, etc.) and display of information through a touchpanel screen, for example. Such a screen may be a touch input LCDscreen, for example, as is well known to those of ordinary skill in theart. Further, the navigation device 200 can also include any additionalinput device 220 and/or any additional output device 241, such as audioinput/output devices for example.

FIGS. 4A and 4B are perspective views of a navigation device 200. Asshown in FIG. 4A, the navigation device 200 may be a unit that includesan integrated input and display device 290 (a touch panel screen forexample) and the other components of FIG. 2 (including but not limitedto internal GPS receiver 250, microprocessor 210, a power supply, memorysystems 230, etc.).

The navigation device 200 may sit on an arm 292, which itself may besecured to a vehicle dashboard/window/etc. using a suction cup 294. Thisarm 292 is one example of a docking station to which the navigationdevice 200 can be docked.

As shown in FIG. 4B, the navigation device 200 can be docked orotherwise connected to an arm 292 of the docking station by snapconnecting the navigation device 292 to the arm 292 for example. Thenavigation device 200 may then be rotatable on the arm 292, as shown bythe arrow of FIG. 4B. To release the connection between the navigationdevice 200 and the docking station, a button on the navigation device200 may be pressed, for example. Other equally suitable arrangements forcoupling and decoupling the navigation device to a docking station arewell known to persons of ordinary skill in the art.

Referring now to FIG. 5 of the accompanying drawings, the memoryresource 230 stores a boot loader program (not shown) that is executedby the processor 210 in order to load an operating system 470 from thememory resource 230 for execution by functional hardware components 460,which provides an environment in which application software 480 can run.The operating system 470 serves to control the functional hardwarecomponents 460 and resides between the application software 480 and thefunctional hardware components 460. The application software 480provides an operational environment including the GUI that supports corefunctions of the navigation device 200, for example map viewing, routeplanning, navigation functions and any other functions associatedtherewith.

In use, the PND 200 contains in the memory 230, or elsewhere, map datarepresenting navigable routes within the area covered by the map data.An example of such a navigable route 600 is shown in FIG. 6. In theexample being described, FIG. 6 shows a road 602 stretching from pointTA to TB together with a so-called ‘T-junction’ connecting the road 602to point TC via a second road 604.

For the PND 200 to have utility it is used in an area covered by the mapdata. As it is moved within the area the PND 200 periodically determinesits position from the GPS system as described above and records thatposition. This is represented in FIG. 6 by the dots 606 shown therein.There are inherent inaccuracies within the system and it is possible forthe PND 200 to determine that its position is outwith the road segment602, 604 as exemplified by the dot 606 a. This can be accounted for inlater processing as described hereinafter and will be fully appreciatedby the skilled person.

In the route shown in FIG. 6, the PND 200 makes a journey 607 from pointTA toward point TB and takes a left turn at the T-junction to turn ontoroad 604 toward point TC. In order to make this turn, the vehicle inwhich the PND 200 is travelling decelerates in the zone 608, representedby the grey box. This zone 608 may be thought of as a deceleration (orpre intersection) zone or a pre-maneuver path. The vehicle then makesthe turn and subsequently accelerates up to speed whilst in the zone 610(again represented by the grey box). Thus, the zone 610 may be thoughtof as an acceleration (or post intersection) zone or a post-maneuverpath.

Thus, in the journey represented by FIG. 6, the vehicle in which the PND200 is situated travels along a road segment, makes a turn and travelsalong a second road segment.

In some embodiments, data is held within the map data for each roadsegment (having a linear spatial extent) giving what may be termed aspeed profile for that segment which may be provided in terms of averagespeed values; In other embodiments, the speed profile may alternativelybe modeled equivalently as an average transit time for that roadsegment, taking into account the length of the road segment at hand.Given that junctions/intersections/road crossing have no, or a lessclear and possible artificial, spatial extent in a network model of theroad network, intersection maneuver profiles are not readily expressedin terms of average speeds, are more readily expressed in terms oftransit time or a set of transit times. It may also be possible toexpress the transit time through the intersection as a transit timeoffset, complementing the sum of road segment transit times along acertain itinerary which is “lost” when slowing down, waiting, andaccelerating or the like.

A set of transit times may be thought of as a transit time profile. Sucha transit time profile may provide transit times at predeterminedintervals. For example the transit time profile may provide the transittime at hour long intervals.

Maneuver-related transit time costs may be associated with some or alltrajectories, or a subset of significant trajectories, along which avehicle can drive through an intersection.

An example of relevant trajectories is reviewed hereafter. Theintersection shown in FIG. 6 is relatively simply and a more complexintersection is shown in FIG. 7 and is what would generally be termed across-roads. Whether or not turn restrictions are taken intoconsideration and thus only allowable driving maneuvers are addressed isan implementation choice.

For a 4-way intersection (ie a cross roads) involving road segments A,B, C, and D (with A←→C having right of way), the following trajectorieswould be the most obvious candidates for transit time profiles: B→C,B→D, B→A, D→A, D→B, D→C.

Additionally, there may be further candidates (assuming right-handtraffic in this example), A→B and C→D which as left turns may take anadditional time. For example, it is conceivable that a further half aminute may be taken in such a maneuver during heavy traffic.

Such information is a property of the two road segment both before theturn due to a deceleration zone (e.g., A) & after the turn due to anacceleration zone (e.g., B). Attaching a “transit time cost” attributeto A or B, respectively, does not model the turn appropriately since thetime taken to make the turn is a property of both road segments;conveniently therefore, the time is an attribute of the directed pairA→B. That is the transit time through the intersection is dependent uponthe route taken through the intersection (as well as on other factorssuch as density of traffic, etc.).

Also, there may be u-turn cases to be covered, such as A→A or C→C whenassuming for instance medians, traffic islands, or other kinds ofdividers between opposite driving directions.

Finally, the balance of all pairs not included above may be covered(explicitly, or implicitly by means of defaults): A→C, A→D, B→B, C→A,C→B, D→D.

In more complicated cases of complex intersections involvingmulti-carriageways, slip roads, etc. the model may even relate to 3 ormore segments as expanded upon in relation to FIG. 8 which shows anexample of a 4-way involving multi-carriage ways, relevant trajectoriesoften involve intersection-internal connectors—namely AC, DB, CA, andBD—, for the moment disregarding potential additional maneuver-specificturn lanes or slip roads. As before, A←→C (short-hand for A2→C2 andC1→A1) is assumed having right of way and right-hand traffic.

Most obvious candidates for transit time profiles would be:

B1→BD→AC→C2, B1→BD→D1, B1→A1, D2→DB→CA→A1, D2→DB→B2, D2→C2.

Further candidates may be: A2→AC→DB→B2 and C1→CA→BD→D1, which as leftturns may be affected by additional wait time during heavy traffic alongA←→C.

Also, there may be u-turn cases to be covered, including A1→ACΔDB→CA→A2and C1→CA→BD→AC→C2 as well as B1→BD→AC→DB→B2 and D2→DB→CA→BD→D1, whichlike before-mentioned left turns may be affected by additional wait timeduring heavy traffic along A←→C.

Finally, the balance of all pairs not included above may be covered(explicitly, or implicitly by means of defaults):

A2→AC→C2, A2→D1, C1→CA→A1, C1→B2.

Considering a driver coming along A2 and wanting to turn into B2,heavy-traffic on C1→A1 might cause a significant amount of wait timewhile residing on DB. Similarly, a wait time on DB may occur for adriver approaching from D2, with a destination towards A1 or B2.Embodiments of this invention allow such cases to be allowed for whenestimating a journey time across that intersection. This may includewaits at traffic light regulated intersections of multi-carriageways,etc.

In use, the speed profile associated with the road segments and routesthrough intersections is created from position data and in particularfrom the trips that have been derived from the GPS traces as describedabove. In the embodiment being described such position data has beencollected from PND's and the GPS receivers therein. However, this neednot be the case.

There may be several specialization methods available for determiningcomprehensive data representing transit time delays through a particularintersection and two such specialization methods are outlined below.

The first method targets individualized transit time profiles andanalyses position data to assess the time implications for variousintersection trajectories and attempts to provide the transit timeeffect of a lowered average speed along a given maneuver in a given timeinterval.

This method may be broken down into a number of sub-tasks, each of whichalso comprises a number of steps. In the first sub-task trips areassociated with intersections between the road segments held in the mapdata.

For this, the set of trips is determined that is relevant for a givenintersection. This may comprise the following steps in this or modifiedorder (described with reference to FIG. 10):

-   -   i. Determine connectivity to/from a given intersection using the        map data; this results in a sub-network for the intersection at        hand (i.e. its “coboundary” in terms of all road segments        topologically connected with the intersection).    -   ii. Apply spatial filtering of GPS fixes within the trips being        processed by using a bounding box around the intersection at        hand; the size of the bounding box may be chosen based on the        spatial extent of the intersection's sub-network or based on        default dimensions optimized for data retrieval or other        reasons. That is, a determination is made of the which GPS fixes        correspond to a portion of a trip passing through an        intersection —step 1000. The skilled person will appreciate that        a GPS fix corresponds, in embodiments using GPS, to a vehicle        position at a given moment in time.    -   iii. Perform map-matching for spatially-filtered GPS fixes by        determining for each of the GPS fixes the nearest position (if        applicable) on a road segment of the corresponding        intersection's sub-network (ie GPS fixes such as 606 a would be        associated with the nearest road segment 602). Map-matching may        deploy more sophisticated matching criteria, including but not        limited to: GPS & map accuracy conditions; geometric proximity;        GPS speed & map attribute proximity; GPS & road segment        directionality; dependencies between neighboring data points.        This may also be considered to be part of step 1000.

Alternatively, the generation of transit time profiles may takeadvantage of position data processing that has been performed for otherpurposes, such as for the generation of speed profiles for roadsegments, which may already provide—as a spin-off for transit timeprofiles a map-matched layer of GPS traces and/or trips.

Further position data processing steps for a given intersection include(steps in this or modified order):

-   -   iv. Determine for each spatially-filtered and map-matched trip        its intersection trajectory, in terms of its path of traversed        road segments corresponding to the map-matched GPS fixes. This        is shown as step 1002 and may be thought of as classifying the        position data related to the intersection according to a route        through the intersection.    -   v. Cluster all trips based on their trajectory through the        intersection, leading to a categorization of trips by driving        maneuver (which may be thought of as “maneuver-matched GPS        trips”). GPS trips which happen to start or end at the        intersection at hand are, in this embodiment, discarded. This        may be thought of as being part of step 1002.    -   vi. Determine the (minimum) length of the maneuver-matched GPS        trips to be taken into account; In order to achieve this the        following criteria may be applied: sufficient GPS fix coverage        before (respectively after) a given intersection shall extend        across        -   at least one whole road segment, or possibly multiple            connected road segments, for which a speed profile is            established; or        -   alternatively a certain predetermined distance D (say            roughly 150 m) which may be subject to fine-tuning; or        -   distance D as minimum road segment length threshold for            selecting one or multiple whole road segments.    -   vii. Perform core segmentation of each maneuver-matched GPS trip        to determine the primary sub-path:        -   Pre-maneuver path, comprising GPS fixes matched to            approaching road segment(s);    -   viii. Optionally, perform more comprehensive segmentation of        each maneuver-matched GPS trip to further determine other        sub-path(s):        -   Inter-maneuver path, comprising GPS fixes matched to road            segment(s) modeled as part of the intersection            representation in the map database;        -   and/or        -   Post-maneuver path, comprising GPS fixes matched to            departing road segment(s);        -   These steps are shown within step 1004.        -   At sub-path borders, a GPS fix may belong to both,            pre-maneuver path & inter-maneuver path or inter-maneuver            path & post-maneuver path, respectively. In the (common)            case of “simple” intersections/road-crossings, such as the            above-mentioned ABCD example shown in FIG. 7, the            inter-maneuver path of a given GPS trace may be “empty” and            its GPS fixes either belong to its pre- or post-maneuver            path.    -   ix. Apply secondary clustering of maneuver-matched GPS trips on        the basis of a predefined time interval scheme, such as discrete        time slots per time of day/day of week, in order to establish a        normal transit time profile which corresponds to recurring        patterns.        -   The discrete time slots may for example correspond to            roughly hour long time bins so that, for example, each            maneuver-matched GPS trip that occurred between a first time            (eg 9 am) and a second time (eg 9:59 am) are placed within            the same time bin. The skilled person will appreciate that            the time bins may have other lengths other than 1 hour—for            example roughly any of the following: 10 minutes, 15            minutes, 20 minutes, 30 minutes, 45 minutes, 2 hours, 6            hours, 12 hours or any time in between these lengths.        -   This is shown in step 1006 and may be thought of as            averaging the time within predetermined time periods, which            in this example are of a predetermined length.    -   x. Optionally, apply a parallel secondary clustering of the same        maneuver-matched GPS trips on the basis of irregular time        pattern that do not occur daily/weekly, such as national        holidays or festivals, in order to establish an abnormal transit        time profile, which corresponds to irregular patterns and        which—if applicable—overwrite a normal transit time profile.        Maneuver-matched GPS trips taken into account for abnormal        transit time profiles are excluded from secondary clusters        created for normal transit time profiles.        -   Such a secondary parallel clustering would allow the transit            time profile for an intersection to be mapped for days for            which traffic flow does not fit a ‘normal’ pattern such as a            national holiday, etc. Thus, when the speed profile is later            used for route planning it is possible to use a transit time            profile for that intersection that more accurately reflects            the likely traffic flow irrespective as to whether the            traffic flow for that day is likely to be ‘normal’ or            ‘abnormal’.    -   xi. Assess temporal behavior of pre-maneuver GPS fixes by        averaging over each secondary cluster of maneuver-matched GPS        trips. In particular, assess whether one can observe a drop in        average speed of pre-maneuver GPS fixes (as a comparison with        the speed profile of the corresponding road segment(s)).        -   If assessment is positive (ic the speed in the secondary            cluster is lower than the average speed for that road            segment as a whole), determine a pre-maneuver time penalty            for the assessed maneuver & time interval at hand, which            equates to the length of the corresponding road segment(s)            divided by the absolute difference of the average speed            comparison.        -   On the contrary, if assessment is negative (ie there is no            speed difference between the secondary cluster and the            average speed of that road segment), the pre-maneuver time            penalty is declared zero (and/or flagged as not applicable)            for the assessed maneuver & time interval at hand.    -   xii. In a similar way, if comprehensive segmentation of        post-maneuver paths is applicable, assess temporal behavior of        post-maneuver data points by averaging over each secondary        cluster of maneuver-matched GPS traces.        -   If assessment is positive (ie the speed in the secondary            cluster is lower than the average speed for that road            segment), then determine a post-maneuver time penalty for            the assessed maneuver & time interval at hand, which equates            to the length of the corresponding road segment(s) divided            by the absolute difference of the average speed comparison.        -   On the contrary, if assessment is negative (ie the speed in            the secondary cluster is the same or higher than the average            speed for that road segment), then the post-maneuver time            penalty is declared zero (and/or flagged as not applicable)            for the assessed maneuver & time interval at hand.    -   xiii. Alternatively, if comprehensive segmentation of        post-maneuver paths is applicable, assess combined temporal        behavior of pre-maneuver & post-maneuver GPS fixes by averaging        over each secondary cluster of maneuver-matched GPS trips. In        particular, assess whether one can observe, on average, a        significant time gap between the last (or last few) time        stamp(s) for the GPS fixes of any given pre-maneuver path and        the first (or first few) time stamp(s) of the GPS fixes of the        related post-maneuver path.        -   If assessment is positive, determine a pre-post-maneuver            time penalty for the assessed maneuver & time interval at            hand, which equates to the averaged overall time gap,            including the (implicit) inter-intersection delay, if            applicable.        -   On the contrary, if assessment is negative, the            pre-post-maneuver time penalty is declared zero (and/or            flagged as not applicable) for the assessed maneuver & time            interval at hand.    -   xiv. In addition or alternatively, if comprehensive segmentation        of inter-maneuver paths is applicable, assess speed behavior of        inter-maneuver GPS fixes (if existing) by averaging over each        secondary cluster of maneuver-matched GPS traces. In particular,        assess whether the average of all speeds associated with the        inter-maneuver data points is significantly lower than the        average speed of the related pre- and post-maneuvers (as a        comparison with the speed profile of the corresponding road        segment(s), whereby all average speeds are determined according        to the same principles).        -   If assessment is positive, determine a relative            inter-maneuver time penalty for the assessed maneuver & time            interval at hand, which equates to the length of the inter            maneuver path divided by the absolute difference of the            average speed comparison. In addition or alternatively,            determine an absolute inter-maneuver time penalty for the            assessed maneuver & time interval at hand, which equates to            the length of the inter maneuver path divided by the average            speed for the inter-maneuver path.        -   On the contrary, if assessment is negative, the            inter-maneuver time penalty is declared zero (and/or flagged            as not applicable) for the assessed maneuver & time interval            at hand.

A final set of GPS fix processing steps for a given intersectioninclude:

-   -   xv. Validate the statistical significance and/or confidence of        each derived time penalty for a given secondary cluster of        maneuver-matched GPS trips and for a given time interval. That        is the averages derived from the clustering are analysed to        determine whether the data that generated the averages meets        predetermined quality criteria step 1008.        -   If validation indicates that position data is too coarse,            apply a fall-back strategy (which may be termed “gap            filling”); reiterate the secondary clustering step (as well            subsequent temporal maneuver behavior assessment step(s)) by            applying a lower-resolution time interval scheme (either for            the time interval at hand, or for similar or all time            intervals). That is if previously the length of time of a            time bin was one hour then a new, longer, length (for            example initial a time bin of 3 hours may be set) in order            that a greater number of GPS trips fall into the longer time            bin. If the position data is still too coarse then further            lengthening of the time bins may occur.            -   In a primary fall-back response, utilize generalization                towards bigger samples of maneuver-matched GPS trips                spanning a longer time period, such as rush-hours vs                rest of day time vs night time. Such merging of time                intervals may be based on traffic density patterns that                can be observed for speed profiles for road segments.            -   As a secondary fall-back response (assuming a persisting                lack of satisfactory GPS fix coverage and/or density),                utilize classification of intersections (rather than                trying to form a speed profile for each individual                intersection) as a basis for comparison and harmonized                treatment such that a speed profile is developed for a                set of intersections having similar characteristics. In                this way, transit time profiles for intersections with                same/similar characteristics (such as road class, form                of way, speed profiles of connected road segments, urban                vs non-urban setting, and the like) may be shared for                gap filling across cases with insufficient GPS fixes.            -   As a third fall-back response, the second specialization                method of determining the transit time delay details an                approach for intersection turn categorization, as                described further below.        -   As an option, if validation indicates that position data has            potential for higher granularity, reiterate the secondary            clustering step (as well subsequent temporal maneuver            behavior assessment step(s)) by applying a higher-resolution            time interval scheme (either for the time interval at hand,            or for similar or all time intervals).    -   xvi. Determine the time offset (total “time loss”), measured in        a given time unit, for a given cluster of maneuver-matched GPS        trips in a given time interval (ie time bin). Different        strategies may be applied as follows:        -   Most commonly, based on core segmentation of pre-maneuver            paths, the time offset equals the pre-maneuver time penalty.            The skilled person will appreciate that most time lost at an            intersection is within the pre-maneuver path (608 in FIG. 6)            in which the deceleration occurs and in which any queuing is            also likely to occur.        -   Alternatively, based on comprehensive segmentation including            inter-maneuver paths and/or post-maneuver paths (as            appropriate), the time offset equals the sum of pre-maneuver            time penalty; post-maneuver time penalty; relative            inter-maneuver time penalty (if applicable).        -   As a second alternative, also based on comprehensive            segmentation including inter-maneuver paths and/or            post-maneuver paths (as appropriate) and further            anticipating the scope of speed profiles for road segments            being exclusive of intersection internal connectors, the            time offset equals the sum of pre-maneuver time penalty;            post-maneuver time penalty; absolute inter-maneuver time            penalty (if applicable).        -   As a third alternative, also based on comprehensive            segmentation including at least post-maneuver paths, the            time offset may equal the pre-post-maneuver time penalty.    -   xvii. Assemble a profile of time costs for each intersection        maneuver in terms of time offsets in function of time of day/day        of week, and—if applicable—in function of the abnormal time        interval scheme.

A turn cost profile can be modeled as discretized averages of timeoffsets or as parametric function; time offsets may be specified asabsolute or classified (coded) values, or as parameter of a time offsetfunction.

A dataset of transit time profiles—generated according to above steps orotherwise—is in one embodiment provided as a complete time-dependentturn cost matrix per intersection, covering all combinations ofapproaching road segment departing road segment, or a reduced matrixwith significant turns only (for all or a sub-set of intersections in amap database). Such matrices may be organized in a database, table orlist structure (either together with speed profiles for road segments orseparately); they may by made available as content file(s), softwarelibrary or via web service(s). A dataset of transit time profiles may bestored and managed as integral part of map database content or may betreated as add-on data, such as supplementary look-up tables.

In another embodiment, being equivalent to the previous, the content ofthe turn cost matrix may be recast by transferring time offset profilesinto attribute sets that are attached to the various approaching roadsegments. This means that for every possible maneuver of a particularapproaching road segment in a given road segment direction, topologicaltime cost profile attributes are attached which comprise:

-   -   directionality towards concerned intersection/road crossing,        and/or map data reference (ID) of concerned intersection/road        crossing;    -   map data reference (ID) to applicable departing road segment;    -   time cost profile.

In addition or as further alternative, the before-mentioned model (timecost profiles as attribute sets of approaching road segments) may beadapted such that the actual maneuver semantics in topological terms(“segment A→segment B”) are translated into geographic/geometrical terms(“left turn”, “+90° turn”, “turn towards north-east”, and the like).Each transit time profile would therefore be described by a quantifiedrelative or absolute turn angle, and/or classified relative or absoluteturn semantics. For operational use, such geographic/geometrical timecost profile attributes simplify the map database model for transit timeprofiles as they do not make use of map data references (IDs). Dependingon the targeted usage by an application, this attribute model has bothmerits (e.g., more intuitive representation for ahuman-machine-interface) and demerits (e.g., requiring interpretation bya route planer at run-time).

Geographic/geometrical time cost profile attributes facilitate expandedcapabilities of safety and efficiency application, in that they easilytranslate into visualization properties (possibly in conjunction withother time-dependent and/or dynamic road or environmental properties).Color-coded icons for the range of possible turn maneuvers along a givenstreet or route, or in a given area, may for instance indicate some formof rating of level of throughput.

In a further embodiment, the data representation of turn cost profileswould take into account the main driving direction(s) at a givenintersection/road crossing which typically corresponds to the straight(or close to straight) through-path(s) from any given approaching roadsegment. Main driving directions serve as reference transit timeprofiles, in terms of their respective time offset at a given time/day;the time offset profiles of the remaining maneuvers at theintersection/road crossing at hand, having the same approaching roadsegment, are then represented as differential penalties relative to thereference time offsets. As a result, the applicable differential penaltyfor a given turn and given time interval may be negative or positive,i.e. designating a delay or gain in transit time, respectively (it is animplementation choice whether negative indicates time delay or gain).The actual reference time offsets may be assumed as zero, ordisregarded, by an application in case the speed profiles for roadsegments are assumed to adequately cover speeds/transit times along maindirections (including intersection crossing maneuvers). Differentialpenalty profiles enhance state-of-the-art solutions which otherwise(without time-dependent profiles) have to assume a constant penalty, orsomehow categorized pattern of penalties, for maneuvers other than themain direction(s).

A variation of the before-mentioned embodiment is the use of a “zerotime loss” maneuver, or “least time loss” maneuver, as referencemaneuver for deriving differential penalty profiles for any othermaneuvers at a given intersection and the reference maneuver'sapproaching road segment at hand. Determination of “zero time loss”maneuvers, or “least time loss” maneuvers, can be based on the processfor generating transit time profiles (as described or done another way),which allows to determine which maneuvers are natural continuationswithout, or with the least, slow-down and time offset. Once known, suchnatural continuation(s) are turned into the reference for generatingdifferential penalty profiles for any other turns at a givenintersection and approaching road segment; in this embodiment variation,differential penalties would always be negative (or always positive, asper the implementation choice) and would always designate time delayscompared to the respective fastest, or least impedance, referencemaneuver.

For integrated solutions (transit time profiles managed as part ofrouting applications' road network map database), existing datastructures may be re-used or amended.

-   -   In the case of a routing application using pre-generated turn        cost matrices (for certain or all classes of intersections/road        crossings), static turn cost data may be supplemented/overridden        by time-dependent turn costs.    -   Concerning the equivalent representation of topological time        cost profile attributes, these may or may not correspond to the        way a routing application accesses and processes turn cost data.    -   Geographic/geometrical time cost profile attributes would        typically be a new set of attributes that need to be made        accessible for a routing application.    -   Differential penalty profiles would target the current practice        of time-sensitive routing engines, such as TomTom IQ Routes,        minimizing run-time calculation complexity. The speed profile        associated with a road segment approaching an intersection/road        crossing would be supplemented with a set of additional        maneuver-sensitive transit time profiles that take into account        the differential penalty for a given maneuver possible at the        approached intersection/road crossing at hand. With reference to        the above-mentioned A/B/C/D example of a 4-way crossing, of FIG.        7, and when approaching from A, the speed profile for road        segment A is complemented with two further transit time        profiles, one corresponding to the A→B turn maneuver and another        one corresponding to the A→D turn maneuver (each profile in        terms of a combined profile of speed & differential penalty for        any given time/day). For the main direction A→C, being the        reference maneuver, the differential penalty would be (assumed)        zero.

A second method of determining the transit time delay is now outlinedwith reference to FIG. 9, specialising in regards to a generic transittime cost function based on categorization of turn types.

Any one journey is constructed of many such traversals of road segmentsfollowed by turns. As discussed above, each turn may be thought of as apre-, inter- and post maneuver portion. However, looking at FIG. 9, ajourney from S to E comprises the road segments 900 (SA), 902 (AC) and904 (CE), together with a intersection at A and at C.

Thus, the total journey time, which can be thought of as a time cost, toreach a certain point in the journey is given by the sum of the timeneeded to drive the road segment coming from the previous intersectionplus the turn cost (the time needed to reach that road segment; ie thetransition time from a previous road). Considering FIG. 9 we will denoteby D_(i) _(_) _(j) the driving cost from the intersection i to theintersection j and Tc_(ij) _(_) _(jk) the turn cost to move between theroad segment between intersections i and j and the road segment betweenthe intersections j and k.

In this case the cost to reach the nodes in the graph from the sourcenode S would be:

C _(S)=0

C _(A) =D _(S) _(_) _(A),

C _(B) =D _(S) _(_) _(A) +Tc _(SA) _(_) _(AB) +D _(A) _(_) _(B),

C _(C) =D _(S) _(_) _(A) +Tc _(SA) _(_) _(AC) +D _(A) _(_) _(C),

C _(D) =D _(S) _(_) _(A) +Tc _(SA) _(_) _(AC) +D _(A) _(_) _(C) +Tc_(AC) _(_) _(CD) +D _(C) _(_) _(D),

C _(E) =D _(S) _(_) _(A) +Tc _(SA) _(_) _(AC) +D _(A) _(_) _(C) +Tc_(AC) _(_) _(CE) +D _(C) _(_) _(E)  (equation 1)

The driving time along a road segment is given by the ratio between thelength of the line and the driving speed, for example

$D_{S\_ A} = {\frac{I_{SA}}{v_{SA}}.}$

The length is normally fixed and the speed can be measured from theposition data that is being processed; for example GPS fixes (ie vehiclepositions). In comparison the turn costs are more un-deterministicbecause the turns are punctual in the map representation while, while asdiscussed above, the turning time is distributed over some distance inthe actual driving and is likely to be spread over a plurality of roadsegments. For example, the turning time is made up of the time in thepre- and post-maneuver zones together with time in the intersection (theinter intersection time).

This makes the intersection delay difficult to estimate and the valuescan be different in similar circumstances. In this, the second method, astatistical approach is used and the different turn costs arecategorized into predetermined categories and an average time for theintersection delay is estimated for each category.

The roads have attributes like measured speeds (average or speedprofiles) the road form describing the road configuration (freeway,multi-carriage, single carriage, etc.) or the road importance for theroad network. Considering that an intersection may be viewed as a linkbetween two roads any combination of these attributes could define anintersection category. The more input data that exists then the morecategories that can be considered and the more precise the transit timeshould be estimated. However, there are down sides to having too great anumber of categories in that redundancy may be introduced. There islikely to be a balance between accuracy and being overly complex.

There are several ways to group the turn costs (the time that can beassigned to a route through an intersection as discussed above inrelation to FIG. 7):

For example, it may be possible to use the angle difference between theincoming and the outgoing roads segments. In such an embodiment, anumber of sectors (which in the embodiment being descried is 8 but maybe any other number such as 4, 16, 32, etc) are created and the angulardifference between the road segments at the intersection is used tocategorize the intersection into the category. For example, in thecurrent embodiment between 0 and 44° is category 0; 45° and 89° iscategory 1; 90° and 134° is category 2; 135° and 179° is category 3;180° and 224° is category 4; 225° and 269° is category 5; 270° and 314°is category 6; and 315° and 359° is category 7. Thus, each sectorbecomes a turn cost category.

In other embodiments, it may be possible to additionally, oralternatively, to use the road form, type or importance (the roadclassification) of the incoming and the outgoing road segments to theintersection.

In other embodiments, it may be possible to additionally, oralternatively, to use the difference in the driving speed between theincoming and the outgoing roads to an intersection. This criteria mayoverlap partially with the previous one and may lead to redundancy.

The categories may be map specific or country specific considering thatthe structure of the road network can be quite different from country tocountry.

Each journey may be represented by an equation similar to equation 1.Thus, GPS trips that are collected from position data can be processedto represent each trip by such an equation. Thus, referring to FIG. 11,a first step is to process the position data to generate a set of suchequations. As such a set of linear equations are generated in which theturn costs are unknown variables; the journey time for each road segmentis estimated from the segment speed profiles (which may be obtained frommap data or generated from position data) and the road length. Thejourney total time is given by the difference between the start and theend time.

Processing position data gathered allows a large number of GPS trips tobe analyzed which should imply that the number of equations will exceedthe number of unknowns. The resulting linear system can be solved in aleast square meaning. This means that we will look to find that set ofturn costs that would minimize the error between the predicted transittime and the real transit time (which is know from the GPS trip).

The cost structure is composed of the driving cost (ie time to drive theroad segment) and the turn cost (ic the time lost in the intersection).The total traveling cost S is given by

$S = {{\sum\limits_{i = 1}^{n}D_{i}} + {\sum\limits_{j = 1}^{m}{Tc}_{j}}}$

where D_(i) are the driving time for each road belonging to the road andTc_(j) are the turn costs (expressed in time) along the route. It isassumed that the measured speeds are correct and the method tries tooptimize the estimation through the turn costs.

The method assumes that there are N types of turns defined into the costmodel. Referring to the discussion above, in an embodiment which used 8categories related to the angular relationship between the incoming andoutgoing road segment then N=8. If we denote the types of turns by Tc₁to Tc_(N) than for the route number k we will have

${S_{drv}^{k} - {\sum\limits_{i}D_{i}^{k}}} = {{A_{1}^{k}{Tc}_{1}} + \ldots + {A_{N}^{k}{Tc}_{N}}}$

where S_(drv) ^(k) is the driven time along the route and A₁ ^(k), . . .,A_(N) ^(k) are the frequency of each type of turn in the total turncost for the route k.

For example, in the present embodiment in which N=8, A₁ ^(k) wouldindicate how many times we have a straight driving, A₂ ^(k) a 45 degreesright turn, and so on.

If we consider several routes from k=1, . . . ,L than we will get thefollowing linear system:

$\; {\begin{bmatrix}{S_{drv}^{1} - {\sum\limits_{i}D_{i}^{1}}} \\\vdots \\{S_{drv}^{L} - {\sum\limits_{i}D_{i}^{L}}}\end{bmatrix} = {\begin{bmatrix}A_{0}^{1} & \cdots & A_{N}^{1} \\\vdots & \ddots & \vdots \\A_{0}^{L} & \cdots & A_{N}^{L}\end{bmatrix}\begin{bmatrix}{Tc}_{1} \\\vdots \\{Tc}_{N}\end{bmatrix}}}$

Defining

${A==\begin{bmatrix}A_{1}^{1} & \cdots & A_{N}^{1} \\\vdots & \ddots & \vdots \\A_{1}^{L} & \cdots & A_{N}^{L}\end{bmatrix}},\mspace{20mu} {b = \; {{\begin{bmatrix}{S_{drv}^{1} - {\sum\limits_{i}D_{i}^{1}}} \\\vdots \\{S_{drv}^{L} - {\sum\limits_{i}D_{i}^{L}}}\end{bmatrix}\mspace{14mu} {and}\mspace{14mu} x} = \begin{bmatrix}{Tc}_{1} \\\vdots \\{Tc}_{N}\end{bmatrix}}}$

than the system can be rewritten in compact form as

Ax=b.

As mentioned above, it is assumed that L the number of routes is farbigger than the number of unknown turn cost variables, i.e. L>>N and assuch there is no exact solution.

The linear system presented above may be solved in the Least SquareSense x⁺=A⁺b, where the A⁺ is the Moore-Penrose pseudo-inverse. The turncost vector x⁺ will provide the optimal turn cost values to be used bythe routing in combination with a given map.

The Moore-Penrose pseudo-inverse will helps to calculate a set of valuesfor the unknown turn costs that will gives the minimum of the secondnorm of the error vector, which means that

${\min\limits_{x}\left( {{{Ax} - b}}_{2} \right)} = {{{{Ax}^{+} - b}}_{2} = {\sqrt{\left( {{Ax}^{+} - b} \right)^{T}\left( {{Ax}^{+} - b} \right)}.}}$

This means that it is not possible to calculate a set of turn costs thatwill produce the exact estimated time for the calculated routes but thewe will have an optimal set of values that will minimize the globalestimation error. Thus, a mathematical method has been used to derive anapproximate solution to the set of linear equations (step 1102)

The pseudo-inverse approach is a least square method because we minimizethe second norm of the error.

Thus, this second method determines an average turn costs for the turnsin any one category; eg in the embodiment being descried forintersections having a predetermined angular relationship.

Finally in steps 1010 and 1104 further map data is created in which thetransit time for any intersections that have had that propertycalculated is associated with that intersection. Such association may beexplicitly (cg providing a transit time or set of transit times in alook-up table, as part of the map data, etc.) or by defining amethodology from which the transit time or set of transit times may bededuced (eg. by providing an offset to a base time which allows thetransit time to be calculated).

The skilled person will appreciate that, whilst the term GPS data hasbeen used to refer to positioning data derived from a GPS globalpositioning system as for example described in relation to FIG. 1. Otherpositioning data could be processed in a manner similar to the methodsas described herein. Thus, term GPS data may be replaceable with thephrase positioning data. Such positioning data, could for example bederived from position data derived from mobile phone operation, datareceived at toll barriers, data obtained from induction loops embeddedin roads, data obtained from number plate recognition system, dataobtained from car-to-car communication, data obtained from on-boardsensors (e.g., a laser scanner) observing surrounding mobile andstationary objects and supporting relative positioning, or any othersuitable data.

It will also be well understood by persons of ordinary skill in the artthat whilst the embodiment described herein implement certainfunctionality by means of software, that functionality could equally beimplemented solely in hardware (for example by means of one or moreASICs (application specific integrated circuit)) or indeed by a mix ofhardware and software. As such, the scope of the present inventionshould not be interpreted as being limited only to being implemented insoftware.

Lastly, it should also be noted that whilst the accompanying claims setout particular combinations of features described herein, the scope ofthe present invention is not limited to the particular combinationshereafter claimed, but instead extends to encompass any combination offeatures or embodiments herein disclosed irrespective of whether or notthat particular combination has been specifically enumerated in theaccompanying claims at this time.

1-13. (canceled)
 14. A method of creating data for a map from positiondata derived from the positions of a plurality of vehicles traversing aroad network over a period of time in an area covered by the map, themap comprising a plurality of segments, each segment representing aportion of the road network, and each segment having a length and anassociated speed profile indicative of average speeds of travel ofvehicles along the portion of the road network represented by thesegment at a plurality of time periods, the map further comprisingintersections between segments representing intersections in the roadnetwork, each intersection having a plurality of maneuvers associatedtherewith, the method comprising using processing circuitry to: use aclassification scheme to classify each maneuver at a plurality ofintersections into a one of a plurality of categories, each categoryrepresenting maneuvers at intersections having similar characteristics;and determine a transit time for maneuvers in each category by:analyzing the position data to determine a real transit time for aplurality of vehicle trips on the road network; determining, for each ofthe vehicle trips, a plurality of segments and a plurality maneuversforming a route through the map; defining, for each of the vehicletrips, an equation giving a predicted transit time for the vehicle trip,the equation being a sum of transit times along each of the plurality ofsegments and transit times for each of the plurality of maneuvers,wherein the transit time for each of the plurality of segments isdetermined using the length of the segment and the average speed fromthe speed profile for a respective time period, and wherein the transittime for maneuvers in each category form unknown parameters in theequation; and using the real transit time and the equations to determinea determined transit time for maneuvers in each category.
 15. The methodaccording to claim 14, wherein the determined transit time for themaneuvers in each category is an approximate solution to each of theequations that is determined using a mathematical approximation thatincludes minimizing an error in the equation.
 16. The method accordingto claim 15, wherein minimizing the error in the equation comprisesusing a least squares solution.
 17. The method according to claim 14,wherein the classification scheme comprises at least one of: an angledifference between the incoming and outgoing segments representing themaneuver, wherein each category represents a range of angles; a form,type or importance of the incoming and outgoing segments representingthe maneuver; and a difference in driving speed between the incoming andoutgoing segments representing the maneuver.
 18. The method according toclaim 14, wherein the classification scheme comprises a time in which amaneuver is performed, wherein each category represents at least apredetermined time period, and wherein the method comprises using aprocessing circuitry to: calculate a transit time profile for eachcategory, the transit time profile comprising a plurality of transittimes, each transit time being indicative of an average time to performa maneuver at an intersection within the category in a predeterminedtime period.
 19. A machine arranged to create data for a map, the mapcomprising a plurality of segments, each segment representing a portionof a road network in an area covered by the map, and each segment havinga length and an associated speed profile indicative of average speeds oftravel of vehicles along the portion of the road network represented bythe segment at a plurality of time periods, the map further comprisingintersections between segments representing intersections in the roadnetwork, each intersection having a plurality of maneuvers associatedtherewith, the machine comprising processing circuitry arranged toprocess position data derived from the positions of a plurality ofvehicles traversing the road network over a period of time, theprocessing circuitry being programmed to: use a classification scheme toclassify each maneuver at a plurality of intersections into a one of aplurality of categories, each category representing maneuvers atintersections having similar characteristics; and determine a transittime for maneuvers in each category by: analyzing the position data todetermine a real transit time for a plurality of vehicle trips on theroad network; determining, for each of the vehicle trips, a plurality ofsegments and a plurality maneuvers forming a route through the map;defining, for each of the vehicle trips, an equation giving a predictedtransit time for the vehicle trip, the equation being a sum of transittimes along each of the plurality of segments and transit times for eachof the plurality of maneuvers, wherein the transit time for each of theplurality of segments is determined using the length of the segment andthe average speed from the speed profile for a respective time period,and wherein the transit time for maneuvers in each category form unknownparameters in the equation; and using the real transit time and theequations to determine a determined transit time for maneuvers in eachcategory.
 20. The machine according to claim 19, wherein the determinedtransit time for the maneuvers in each category is an approximatesolution to each of the equations that is determined using amathematical approximation that includes minimizing an error in theequation.
 21. The machine according to claim 20, wherein minimizing theerror in the equation comprises using a least squares solution.
 22. Themachine according to claim 19, wherein the classification schemecomprises at least one of: an angle difference between the incoming andoutgoing segments representing the maneuver, wherein each categoryrepresents a range of angles; a form, type or importance of the incomingand outgoing segments representing the maneuver; and a difference indriving speed between the incoming and outgoing segments representingthe maneuver.
 23. The machine according to claim 19, wherein theclassification scheme comprises a time in which a maneuver is performed,wherein each category represents at least a predetermined time period,and wherein the processing circuitry is further programmed to: calculatea transit time profile for each category, the transit time profilecomprising a plurality of transit times, each transit time beingindicative of an average time to perform a maneuver at an intersectionwithin the category in a predetermined time period.
 24. A non-transitorycomputer readable storage medium storing instructions that, whenexecuted by processing circuitry in a machine, cause the processingcircuitry to perform a method for creating data for a map from positiondata derived from the positions of a plurality of vehicles traversing aroad network over a period of time in an area covered by the map, themap comprising a plurality of segments, each segment representing aportion of the road network, and each segment having a length and anassociated speed profile indicative of average speeds of travel ofvehicles along the portion of the road network represented by thesegment at a plurality of time periods, the map further comprisingintersections between segments representing intersections in the roadnetwork, each intersection having a plurality of maneuvers associatedtherewith, the method comprising: using a classification scheme toclassify each maneuver at a plurality of intersections into a one of aplurality of categories, each category representing maneuvers atintersections having similar characteristics; and determining a transittime for maneuvers in each category by: analyzing the position data todetermine a real transit time for a plurality of vehicle trips on theroad network; determining, for each of the vehicle trips, a plurality ofsegments and a plurality maneuvers forming a route through the map;defining, for each of the vehicle trips, an equation giving a predictedtransit time for the vehicle trip, the equation being a sum of transittimes along each of the plurality of segments and transit times for eachof the plurality of maneuvers, wherein the transit time for each of theplurality of segments is determined using the length of the segment andthe average speed from the speed profile for a respective time period,and wherein the transit time for maneuvers in each category form unknownparameters in the equation; and using the real transit time and theequations to determine the transit time for maneuvers in each category.25. The non-transitory computer readable storage medium according toclaim 24, wherein the determined transit time for the maneuvers in eachcategory is an approximate solution to each of the equations that isdetermined using a mathematical approximation that includes minimizingan error in the equation.
 26. The non-transitory computer readablestorage medium according to claim 25, wherein minimizing the error inthe equation comprises using a least squares solution.
 27. Thenon-transitory computer readable storage medium according to claim 24,wherein the classification scheme comprises at least one of: an angledifference between the incoming and outgoing segments representing themaneuver, wherein each category represents a range of angles; a form,type or importance of the incoming and outgoing segments representingthe maneuver; and a difference in driving speed between the incoming andoutgoing segments representing the maneuver.
 28. The non-transitorycomputer readable storage medium according to claim 24, wherein theclassification scheme comprises a time in which a maneuver is performed,wherein each category represents at least a predetermined time period,and wherein the method comprises: calculating a transit time profile foreach category, the transit time profile comprising a plurality oftransit times, each transit time being indicative of an average time toperform a maneuver at an intersection within the category in apredetermined time period.
 29. A method of creating data for a map fromposition data derived from the positions of a plurality of vehiclestraversing a road network over a period of time in an area covered bythe map, the map comprising a plurality of segments, each segmentrepresenting a portion of the road network, the map further comprisingintersections between segments representing intersections in the roadnetwork, each intersection having a plurality of maneuvers associatedtherewith, the method comprising using processing circuitry to: use aclassification scheme to classify each maneuver at the intersectionsinto a one of a plurality of categories, each category representingmaneuvers at intersections having similar characteristics; analyze theposition data to determine, for each of a plurality of theintersections, position data corresponding to a portion of a vehicletrip that passed through the intersection, and classify the determinedposition data according to the maneuver made by the vehicle when itpassed through the intersection and a time at which the maneuver wasperformed; and analyze the classified position data to determine thetime taken to perform the maneuver, and average the determined times formaneuvers in the same category to calculate a transit time profile foreach category, the transit time profile comprising a plurality oftransit times, each transit time being indicative of an average time toperform a maneuver at an intersection within the sector category in apredetermined time period.
 30. The method according to claim 29, whereinthe characteristics of the intersections representing the maneuvers ineach category are similar in respect of at least one of: road class;form of way; urban and non-urban setting; and speed profiles.
 31. Themethod according to claim 29, further comprising determining at leastone of: a route from an origin to a destination using the created data;and a journey time for a route from an origin to a destination using thecreated data.
 32. A machine creating data for a map from position dataderived from the positions of a plurality of vehicles traversing a roadnetwork over a period of time in an area covered by the map, the mapcomprising a plurality of segments, each segment representing a portionof the road network, the map further comprising intersections betweensegments representing intersections in the road network, eachintersection having a plurality of maneuvers associated therewith, themachine comprising processing circuitry configured to: use aclassification scheme to classify each maneuver at the intersectionsinto a one of a plurality of categories, each category representingmaneuvers at intersections having similar characteristics; analyze theposition data to determine, for each of a plurality of theintersections, position data corresponding to a portion of a vehicletrip that passed through the intersection, and classify the determinedposition data according to the maneuver made by the vehicle when itpassed through the intersection and a time at which the maneuver wasperformed; and analyze the classified position data to determine thetime taken to perform the maneuver, and average the determined times formaneuvers in the same category to calculate a transit time profile foreach category, the transit time profile comprising a plurality oftransit times, each transit time being indicative of an average time toperform a maneuver at an intersection within the sector category in apredetermined time period.
 33. The machine according to claim 32,wherein the characteristics of the intersections representing themaneuvers in each category are similar in respect of at least one of:road class; form of way; urban and non-urban setting; and speedprofiles.
 34. The machine according to claim 32, wherein the processingcircuitry is further configured to determine at least one of: a routefrom an origin to a destination using the created data; and a journeytime for a route from an origin to a destination using the created data.35. A non-transitory computer readable storage medium storinginstructions that, when executed by processing circuitry in a machine,cause the processing circuitry to perform a method for creating data fora map from position data derived from the positions of a plurality ofvehicles traversing a road network over a period of time in an areacovered by the map, the map comprising a plurality of segments, eachsegment representing a portion of the road network, the map furthercomprising intersections between segments representing intersections inthe road network, each intersection having a plurality of maneuversassociated therewith, the method comprising: using a classificationscheme to classify each maneuver at the intersections into a one of aplurality of categories, each category representing maneuvers atintersections having similar characteristics; analyzing the positiondata to determine, for each of a plurality of the intersections,position data corresponding to a portion of a vehicle trip that passedthrough the intersection, and classify the determined position dataaccording to the maneuver made by the vehicle when it passed through theintersection and a time at which the maneuver was performed; andanalyzing the classified position data to determine the time taken toperform the maneuver, and average the determined times for maneuvers inthe same category to calculate a transit time profile for each category,the transit time profile comprising a plurality of transit times, eachtransit time being indicative of an average time to perform a maneuverat an intersection within the sector category in a predetermined timeperiod.
 36. The non-transitory computer readable storage mediumaccording to claim 35, wherein the characteristics of the intersectionsrepresenting the maneuvers in each category are similar in respect of atleast one of: road class; form of way; urban and non-urban setting; andspeed profiles.
 37. The non-transitory computer readable storage mediumaccording to claim 35, further comprising determining at least one of: aroute from an origin to a destination using the created data; and ajourney time for a route from an origin to a destination using thecreated data.